Demographic, clinical characteristics, polycystic ovarian syndrome phenotypes and predictors of anti-mullerian hormone among women with PCOS at a Fertility Center in Ghana: A 5-year retrospective study

Demographic, clinical characteristics, polycystic ovarian syndrome phenotypes and predictors of anti-mullerian hormone among women with PCOS at a Fertility Center in Ghana: A 5-year retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Demographic, clinical characteristics, polycystic ovarian syndrome phenotypes and predictors of anti-mullerian hormone among women with PCOS at a Fertility Center in Ghana: A 5-year retrospective study Sylvester Yao Lokpo, Moses Kwadzo Gawonyah, Kenneth Ablordey, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6388647/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Polycystic ovarian syndrome (PCOS) is most prevalent among women of reproductive age and is characterized by heterogeneous clinical presentations and demographic variability. This five-year review examined the demographic, clinical characteristics, PCOS phenotypes, and the factors associated with anti-mullerian hormone (AMH) in women with PCOS at a Fertility Center in Accra. Methodology: The study employed a hospital-based retrospective analysis involving women with PCOS at Lister Hospital and Fertility Center, with data extracted from January 2019 to December 2023. Descriptive statistics were used to summarize socio-demographic, biochemical, and clinical parameters. The Rotterdam Criteria were used to classify clinically relevant PCOS phenotypes. Correlation and linear regression analyses were performed to determine predictors of AMH levels. Results: A total of 242 PCOS patients’ records were reviewed. The median age was 31 years. Approximately, 14.9% had diabetes mellitus, 82.6% were overweight/obese, and 48.3% experienced amenorrhea. All participants (100%) had elevated AMH, with 29.4% exhibiting elevated luteinizing hormone to follicle stimulating hormone (LH/FSH) ratio, and 20.8% had hyperprolactinaemia. 85.5%, (n=207) had "elevated AMH + oligo-anovulation" phenotype. 7.4% (n=18) exhibited "elevated AMH + hirsutism" phenotype, while 7.0% (n=17) had the classic phenotype (oligo-anovulation + hirsutism + elevated AMH). BMI was correlated with AMH (r=0.65, p<0.001), with each kg/m² change in BMI resulting in a 0.73 ng/ml increase in AMH levels. Conclusion: Our study provided insight into the demographic and clinical characteristics of women with PCOS, highlighting weight management as a mitigating strategy for hyperactive ovarian disease and need for personalized management strategies to prevent long-term complications. Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Polycystic ovary syndrome (PCOS) is a heterogeneous illness characterized by a mix of signs and symptoms of androgen excess and ovarian dysfunction and it is one of the leading causes of infertility worldwide ( 1 – 3 ). The diagnosis is made after ruling out other specific conditions including hyperprolactinemia and non-classic congenital adrenal hyperplasia ( 1 ). However, the definition of PCOS has been disputed in a variety of disciplines due to the variation in its presentation. For researchers working in both clinical and fundamental science settings, understanding the aetiology of PCOS and differentiating between primary pathological alterations and secondary environmental disturbances remains a persistent difficulty ( 2 ). PCOS is the most common endocrine disorder affecting women of reproductive age worldwide, with the prevalence estimated to be in the range of 4% and 12% ( 4 ). In Nigeria, the prevalence of PCOS among infertile women ranges from 16.7–27.6%, depending on the diagnostic criteria used (National Institute of Health, Rotterdam Classification, or Androgen Excess Society) ( 5 – 7 ). Moreover, a study in Sudan among infertile women found that 32% met the Rotterdam criteria for PCOS ( 8 ). According to Stein and Leventhal ( 9 ), PCOS which was regarded as a gynecological disorder, was now considered a complex endocrinopathy in women of reproductive age owing to its association with multiple metabolic co-morbidities. Women with PCOS typically exhibit hyperandrogenism along with oligomenorrhea, amenorrhea, or infertility ( 10 – 12 ). For women with PCOS, three sets of criteria namely; National Institute of Health, Rotterdam Classification, and Androgen Excess Society have been established. For each of these criteria, different combinations of ovulatory failure, polycystic ovarian morphologic characteristics, and hyperandrogenism constitute unique phenotypes ( 13 , 14 ). Consequently, PCOS can be classified into four distinct phenotypes based on the presence or absence of the three primary features: hyperandrogenism (HA), ovulatory dysfunction (OD), and polycystic ovarian morphology (PCOM) ( 15 ). The classic phenotype which combines the three main features (HA + OD + PCOM) has been associated with the most severe metabolic and reproductive disturbances ( 16 ). The second phenotype combines hormonal imbalances and menstrual irregularities but lacks polycystic ovarian morphology (HA + OD) ( 15 ). The third phenotype features androgen excess and ovarian abnormalities without ovulatory dysfunction (HA + PCOM) while the fourth phenotype (OD + PCOM) is considered the mildest form, presenting with ovulatory dysfunction and polycystic ovarian morphology but without signs of hyperandrogenism ( 15 , 17 , 18 ). The management of clinically relevant phenotypes requires a tailored approach that addresses both reproductive and metabolic concerns. Lifestyle modifications, including diet and exercise, are the first-line approach, especially for overweight individuals, as weight loss improves ovulation and insulin sensitivity ( 19 ). Pharmacological treatments include hormonal contraceptives for cycle regulation and hyperandrogenism, ovulation induction agents for fertility, and insulin sensitizers like metformin for metabolic issues ( 20 ). In severe cases, anti-androgens such as spironolactone may be used ( 21 ). Emerging therapies like inositol supplementation and glucagon-like peptide (GLP)-1 receptor agonists show promise in addressing metabolic dysfunction ( 22 ). Nonetheless, there is currently a gap in research focusing on the demographic and clinical profiles of women with PCOS within the Ghanaian population. Moreover, information about the clinically relevant PCOS phenotypes is lacking and this hinders the development of personalized treatment strategies potentially contributing to suboptimal outcomes in affected women. It is against this background that we sought to retrospectively analyze the demographic, clinical characteristics, PCOS phenotypes and the factors associated with anti-mullerian hormone (AMH) levels in women with clinically diagnosed PCOS at Lister Hospital and Fertility Center in Accra, Ghana based on dated extracted from 2019 to 2023. 2.0 Methodology 2.1 Study design The study was a hospital-based retrospective analysis of secondary data collected on women presenting with PCOS at the Lister Hospital and Fertility Center (LHFC) in Accra. Archival data from January 2019 to December 2023 was extracted for analysis. 2.2 Study site The study was carried out at the LHFC, a subsidiary of Lister Medical Services, which was established on July 1, 2004. It is an ultra-modern private medical center among the most technologically advanced hospitals in West Africa. LHFC has 25 registered beds and provides the following services; laboratory, imaging, nursing- emergency, in-patient, out-patient, public health, theatre-, pharmacy, and fertility. The laboratory department provides a wide range of investigations including hormonal assays, full blood count tests, thyroid function tests, liver function tests, and lipid profiles as well as microbiological tests. The hospital currently has an average attendance of over 2000 annually. 2.3 Study population and sampling technique The study population included women diagnosed of PCOS who sought medical attention at the hospital between January 2019 and December 2023. A convenient sampling technique was used for this study. 2.4 Inclusion and exclusion criteria 2.4.1 Inclusion criteria Premenopausal women aged 18 years and older with PCOS who had records available for review at the hospital during the study period. 2.4.2 Exclusion criteria Women without PCOS diagnosis, aged below 18 years, with a history of endocrinal disorders including those that may mimic PCOS such as Cushion syndrome, hyperprolactinaemia, thyroid disease, congenital adrenal hyperplasia, non-classic adrenal hyperplasia, adrenal secreting tumour, idiopathic hirsutism, and idiopathic hyperandrogenism. 2.5 Sample size determination The sample size was calculated using Cochran’s formula with prevalence of 16.7% ( 5 – 7 ). n= (Z 2 x p x (1-p)/e 2 ( 23 ) = (1.962 x 0.167 x 0.93)/0.052 = 100 Where Z = 1.96 (at 95% confidence interval), e = 0.05, p = prevalence of 16.7% The minimum sample size was 214. However, to improve the statistical power, a total of 242 records were included in this study. 2.6 Data collection instrument and procedure Data were collected using a data extraction sheet. Socio-demographic data (age, occupation, marital status, religion and level of education, employment status) and clinical information including laboratory variables (hormonal profile), body mass index, menstrual irregularities, hirsutism, alopecia, acne, etc, as well as the presence of complications such as diabetes mellitus, hypertension, and cardiovascular disease, were retrieved from the archival records. 2.7 Determination of hormone levels All hormonal assays were performed on mini VIDAS® automated equipment (bioMérieux, France). The assay is based on the principle of immunoassay sandwich method with final fluorescent detection. The sample to be measured is transferred into wells containing a specific antibody labelled with alkaline phosphatase (conjugate). The sample/conjugate mixture is repeatedly cycled through the solid phase receptacle, allowing the target hormone to bind to the antibodies coated on the solid phase receptacle interior while also forming a complex with the conjugate. Unbound components are removed during the washing steps. In the final detection step, the substrate (4-methylumbelliferyl phosphate) is introduced and cycled through the solid phase receptacle. The conjugate enzyme catalyzes the hydrolysis of the substrate into a fluorescent product (4-methylumbelliferone), which is measured at 450 nm. The fluorescence intensity is directly proportional to the concentration of the hormone in the sample. Upon completion of the assay, the instrument automatically calculates the results based on a stored calibration curve and generates a printed report ( 24 ). 2.8 Definitions of operational terms Elevated AMH level was defined as serum levels greater than 3ng/ml. Subclinical hyperthyroidism was defined as low TSH ( 6.16mIU/L) and normal T 3 and T 4 levels ( 25 ). Low FSH level was defined as FSH less than 2.0mIU/ml. Elevated LH level was defined as LH greater than 26.0mIU/ml. Elevated LH/FSH ratio was defined as LH/FSH ratio greater than 2. Low LH/FSH ratio was defined as LH/FSH ratio lower than 1.1 ( 26 ). Hyperprolactinaemia was defined as serum prolactin levels greater than 25ng/ml. Dyslipidaemia was defined using the NCEP-ATP III criteria ( 27 ). BMI (kg/m 3 ) was classified into normal weight; 18.5–24.9, overweight; 25.0-29.9, obesity class I; 30.0-34.9, obesity class II; 35.0-39.9 ( 28 ). 2.9 Data handling and analysis Data were obtained into an Excel Spreadsheet, cleaned, checked for completeness, and exported into Statistical Package for the Social Sciences (SPSS) version 26.0 and MedCalc version 22.0 for analysis. Descriptive statistics were used to summarize the socio-demographic, biochemical, and clinical characteristics of study participants. Continuous variables were described as median with minimum and maximum values. Categorical variables were described as the frequency and corresponding proportions. Correlation and linear regression model analyses were performed to determine the predictors of AMH levels. A p-value < 0.05 was considered statistically significant. 3.0 Results Sociodemographic characteristics of study participants From Table 1 below, a total of 242 records of PCOS patients were reviewed for the 5 years under review. The median age of participants was 31 years, with the least and maximum ages being 18 and 45 years, respectively while most participants (31.4%, n = 76) were within the 31–35 years category. The majority of participants were married (73.1%, n = 177), 177 had attained tertiary level education (73.1%, n = 177) and more than half were Christians (61.2%, n = 148). The employment status showed that a majority of participants (71.1%, n = 172) were unemployed whereas only 28.9% (n = 70) were employed. Table 1 Sociodemographic characteristics of study participants Variables Frequency Percentage (%) Total 242 100.0 Age (years) Median (minimum-maximum) 31.0 (18.0–45.0) 40 9 3.7 Marital status Married 177 73.1 Divorced/Separated 6 2.5 Single 59 24.4 Level of education No education 13 5.4 Primary 7 2.9 Secondary 45 18.6 Tertiary 177 73.1 Religious affiliation Christianity 148 61.2 Islam 74 30.6 Traditional 14 5.8 Hinduism 6 2.5 Employment status Unemployed 172 71.1 Employed 70 28.9 Data presented as the frequency and the corresponding proportions or median with minimum and maximum values. Clinical characteristics of study participants In Table 2 below, the clinical characteristics indicate that diabetes mellitus was present in 14.9% of participants, with 12.0% (n = 29) diagnosed with type 2 diabetes mellitus while 2.9% (n = 7) had type 1 diabetes mellitus. Approximately, 82.6% of participants were either overweight or obese. Menstrual irregularities were prevalent, with 48.3% (n = 117) experiencing amenorrhea and 44.2% (n = 107) reporting oligomenorrhea, while dysmenorrhea was present in 7.4% (n = 18). Hirsutism was observed in 14.5% (n = 35) of participants, with no clinical presentations of alopecia, acne and anxiety. Table 2 Clinical characteristics of study participants Variables Frequency Percentage (%) Comorbidities Diabetes mellitus None 206 85.1 Type 1 7 2.9 Type 2 29 12.0 BMI (kg/m 3 ) Normal 42 17.4 Overweight 93 38.4 Obesity class I 65 26.9 Obesity class II 42 17.4 Symptoms Menstrual History Amenorrhea 117 48.3 Dysmenorrhea 18 7.4 Oligomenorrhea 107 44.2 Hirsutism Absence 207 85.5 Presence 35 14.5 Alopecia None 242 100.0 Acne None 242 100.0 Anxiety None 242 100.0 Data presented as the frequency and the corresponding proportions Hormonal characteristics of study participants From Table 3 below, all participants had elevated anti-Müllerian hormone (AMH) (100%, n = 242). Subclinical hypothyroidism and subclinical hyperthyroidism were present in 0.4%, n = 1 and 0.8%, n = 2, respectively. Elevated luteinizing hormone-follicle stimulating hormone (LH/FSH) ratio and prolactin levels were observed in 29.4%, n = 10 and 20.8%, n = 41 of study participants. Table 3 Hormonal characteristics of study participants Hormones Frequency Percentage (%) Anti-Mullerian Hormone (ng/ml) (n = 242) High 242 100.0 Thyroid Stimulating Hormone (mIU/ml) (n = 240) Normal 237 98.8 Subclinical hypothyroidism (High TSH, Normal T 3 and T 4 ) 1 0.4 Subclinical hyperthyroidism (Low TSH, Normal T 3 and T 4 ) 2 0.8 Luteinizing Hormone (mIU/ml) (n = 44) Normal 41 93.2 Low 1 2.3 High 2 4.5 Follicle Stimulating Hormone (mIU/ml) (n = 35) Normal 34 14.0 Low 1 0.4 Luteinizing Hormone -Follicle Stimulating Hormone Ratio (n = 34) Normal 8 23.5 Low 16 47.1 High 10 29.4 Prolactin (ng/ml) (n = 197) Normal 155 78.7 Low 1 0.5 High 41 20.8 Data presented as the frequency and the corresponding proportions Association between Age and Anti-Mullerian Hormone In Fig. 1 below, there was no significant association between age and AMH levels (r = 0.02, p = 0.714) from the correlation and linear regression analyses. Association between Body Mass Index and Anti- Mullerian Hormone From Fig. 2, the scatter plot shows a significant positive correlation between BMI and AMH levels (r = 0.65; p < 0.65). Moreover, for every 1 kgm 2 change in BMI results in 0.73ng/ml increase in AMH (P < 0.001) PCOS phenotypes among study participants In Fig. 3, the distribution of PCOS phenotypes shows that the majority (85.5%, n = 207) presented with the "elevated AMH + oligo-anovulation" phenotype. 7.4%, n = 18 exhibited the "elevated AMH + Hirsutism" phenotype, while 7.0% (n = 17) had the classic phenotype (Oligo-anovulation + Hirsutism + elevated AMH). 4.0 Discussion Polycystic ovary syndrome (PCOS) predominantly affects women of reproductive age, with varying age distributions across different populations ( 29 ). In our study, the median age of 31 years suggests that PCOS is most commonly diagnosed in women in their late twenties to early thirties. This observation aligns with a study by Bozdag and colleagues ( 30 ) who indicated that PCOS prevalence peaks between 25 and 34 years, coinciding with the reproductive years when symptoms such as menstrual irregularities, infertility, and metabolic disturbances become more pronounced. Moreover, the finding that the majority of participants (31.4%) fell within the 31–35 years range agrees with a previous study highlighting that women often seek medical attention for PCOS-related issues, such as difficulty in conception, during this period ( 31 ). Our findings however contradict those of Alenzi and co ( 18 ) who found a majority of women (31.5%) with PCOS between 18 and 22 years. The disparity between our findings and those reported by Alenzi and co ( 18 ), could be attributed to several factors, including differences in study populations, healthcare access, and diagnostic patterns. According to Keskin and colleagues ( 32 ), younger women between the ages of 18–22 years often seek medical attention when experiencing severe symptoms such as irregular menstruation, acne, or excessive hair growth, leading to higher diagnosis rates in this age group. Early recognition of PCOS in younger women, particularly in adolescents, is crucial for timely intervention to prevent long-term complications such as type 2 diabetes mellitus and cardiovascular disease ( 33 ). Meanwhile, the presence of PCOS in older women nearing menopause suggests that metabolic and endocrine consequences may persist beyond the typical reproductive years, necessitating lifelong management strategies. This assertion was corroborated by Ding et al ( 34 ) who indicated that women with PCOS often continue to exhibit hyperandrogenism and insulin resistance even after menopause, further emphasizing the importance of ongoing medical care. The high prevalence of metabolic disturbances among participants with PCOS agrees with existing evidence linking the condition to insulin resistance and obesity ( 35 ). In our study, 14.9% of participants had diabetes mellitus, with type 2 diabetes mellitus (T2DM) being the predominant form (12.0%). The finding is consistent with a systematic review conducted by Moran et al ( 36 ) who reported the prevalence of T2DM in women with PCOS ranging from 7–40%, depending on ethnicity, age, and BMI. However, the findings contradict a study by Ranathunga and colleagues ( 37 ) who reported a lower rate of diabetes mellitus (6.7%) in women with PCOS. Insulin resistance, a key pathological feature of PCOS, contributes to hyperinsulinemia, which exacerbates ovarian androgen production and metabolic dysfunction, thereby increasing the risk of T2DM ( 38 ). The relatively lower prevalence of T1DM among women with PCOS in our study is somewhat expected as T1DM is an autoimmune disorder that is not directly linked to the metabolic and endocrine dysregulation observed in PCOS ( 39 ). Nevertheless, a previous study suggests that PCOS may still occur in women with T1DM due to insulin therapy-induced hyperinsulinemia, which affects ovarian function ( 40 ). The high prevalence of overweight and obesity (82.6%) among participants in our study reinforces the well-established link between PCOS and weight gain, particularly in populations with high insulin resistance. This finding is slightly higher than the 76.7% reported by Ranathunga et al ( 37 ) in a study conducted in Colombo and 53.3% reported by Mangalath et al ( 41 ) in India, suggesting possible differences in lifestyle, genetic predisposition, or healthcare access between the study populations. Obesity is known to exacerbate PCOS symptoms by worsening insulin resistance, increasing androgen levels, and contributing to metabolic dysfunction, which in turn heightens the risk of diabetes and cardiovascular diseases ( 42 ). The higher prevalence of obesity in our study may also indicate environmental and dietary factors that promote weight gain, including sedentary lifestyles and high-calorie diets, which are increasingly prevalent in Sub-Saharan Africa. Interestingly, no cases of alopecia, acne, or anxiety were reported among our study participants, which contrasts with prior studies that frequently identify these as common clinical features of PCOS ( 43 ). This discrepancy may be attributed to variations in genetic predisposition, environmental factors, or differences in participant self-reporting and clinical assessment methods. Additionally, healthcare-seeking behaviour may play a role, as women with metabolic symptoms might prioritize seeking medical attention for obesity and diabetes mellitus rather than dermatological or psychological concerns. The distribution of PCOS phenotypes in this study reveals that the majority (85.5%) presented with the elevated AMH + oligo-anovulation phenotype, 7.4% exhibited elevated AMH + hirsutism phenotype, while 7.0% had the classic phenotype (oligo-anovulation + hirsutism + elevated AMH). Our study findings contrast with Tatar et al ( 15 ) who reported 47.9% of the classic phenotype, 37.5% of ovarian dysfunction + hyperandrogenism phenotype, and 4.1% of ovarian dysfunction + polycystic ovarian morphology phenotype. This discrepancy may largely stem from differences in the diagnostic criteria and methods used to define PCOS phenotypes. While our study relied on elevated AMH levels as a surrogate marker for ovarian dysfunction, Tatar e t al ( 15 ) incorporated ultrasound-detected polycystic ovarian morphology as part of their diagnostic framework. Such methodological variations can lead to differing classifications, as AMH levels and ovarian morphology, although correlated, may not accurately reflect the underlying ovarian pathology. Furthermore, the variations in PCOS phenotypes may be influenced by lifestyle, environmental, and metabolic differences across populations. Hyperandrogenic phenotypes are more common in Western populations, while ovulatory dysfunction with fewer androgenic symptoms is more prevalent in Asia and the Middle East ( 44 ). The high prevalence of elevated AMH + oligo-anovulation phenotype in our study may be due to the role of AMH as a key marker of ovarian dysfunction in PCOS ( 45 ). These findings emphasize the heterogeneity of PCOS and the need for tailored management strategies. For instance, women with predominant ovarian dysfunction may benefit more from ovulation-inducing therapies, whereas those with hyperandrogenic features might require additional interventions targeting androgen excess ( 46 ). The presence of elevated AMH levels (100%) among participants in our study underscores the strong association between AMH and polycystic ovary syndrome (PCOS). AMH is a hormone secreted by small antral follicles, and it is frequently elevated in PCOS due to an increased follicular pool and disrupted folliculogenesis ( 45 ). Our finding is somewhat expected as AMH serves as a valuable biomarker for diagnosing PCOS, particularly in distinguishing it from other causes of menstrual irregularities ( 31 ). In contrast, the prevalence of thyroid dysfunction was low in our study, with 0.4% and 0.8% of participants exhibiting subclinical hypothyroidism and subclinical hyperthyroidism, respectively. Contrary to our study, Mangalath et al ( 41 ) found 16% each of hypothyroidism and hyperthyroidism. In 2019, a study from Denmark reported that the risk of thyroid disease in PCOS patients was 2.5 times higher than in patients without PCOS ( 47 ). In addition, a review by Palomba et al ( 48 ), posits that PCOS and thyroid disorders are closely related, and their coexistence may identify patients with a higher reproductive and metabolic risk. Interestingly, however, Rojhani et al ( 49 ) found no association between PCOS status and subclinical hypothyroidism. These findings are in line with a study suggesting that thyroid dysfunction is not a major feature of PCOS, although some studies have noted a slightly higher prevalence, potentially due to shared endocrine pathways and autoimmune associations ( 50 ). In addition, an elevated luteinizing hormone (LH)/follicle-stimulating hormone (FSH) ratio was observed in 29.4% of participants, while 20.8% had hyperprolactinaemia. The findings in relation to LH/FSH ratio contrasts with an earlier report of a higher prevalence of LH/FSH dysregulation, exceeding 40%, particularly in the classic PCOS phenotypes ( 51 ). The relatively lower proportion in our study may reflect phenotypic differences, as hyperandrogenic features are often more pronounced in Western populations, where LH hypersecretion is more frequently reported ( 52 ). Hyperprolactinemia, though not a defining characteristic of PCOS, has been documented in a subset of patients, likely due to chronic oestrogen stimulation or stress-related hypothalamic dysfunction ( 53 ). Our study found no significant association between age and AMH levels (r = 0.02, P = 0.714). This is inconsistent with Piouka et al ( 54 ) who found age to be negatively correlated with AMH ( r = − 0.215, P = 0.001) indicating that ovarian reserve diminishes over time ( 55 – 57 ). However, the absence of a significant correlation in our study could be attributed to the relatively narrow age range of participants or the influence of PCOS pathophysiology, where AMH remains persistently elevated despite advancing age. Conversely, our study observed a significant positive correlation between BMI and AMH levels (r = 0.65, p < 0.001), with a 0.73 ng/ml increase in AMH for every 1 kg/m² increase in BMI possibly due to an increased follicular pool and impaired follicular maturation as obesity is associated with increased AMH levels in women with PCOS. Our findings contrasts with a study by Piouka et al ( 54 ) who found BMI to be negatively correlated with AMH, with a -0.71ng/ml decrease in AMH for every 1 kg/m² increase in BMI. Another study also revealed that AMH significantly decreased as BMI increased ( 58 ). The disparity regarding the relationship between BMI and AMH levels in PCOS patients may be attributed to factors such as differences in study populations, methodologies, and underlying metabolic characteristics. Additionally, Piouka et al ( 54 ) and Kloos et al ( 58 ) may have included participants with more advanced metabolic dysfunction, where prolonged obesity-induced ovarian damage leads to reduced follicular reserve and lower AMH secretion. 5.0 Conclusion Our study provides insight into the demographic, clinical, and hormonal characteristics of women with PCOS, highlighting weight management as a possible mitigating strategy for hyperactive ovarian disease. The study further emphasizes the need for personalized management strategies to address the diverse manifestations of PCOS and to mitigate long-term complications such as infertility, diabetes, and cardiovascular disease. 6.0 Limitations Despite the strength of our study regarding a review conducted over five years, certain limitations have been acknowledged. The cross-sectional nature of our study prevents causal inferences. Additionally, the reliance on AMH as a primary diagnostic marker without incorporating an ultrasound assessment of ovarian morphology may have influenced the phenotype classifications. The study population may not be entirely representative of broader PCOS populations, as factors such as ethnicity, lifestyle, and healthcare access can influence symptom presentation. Another limitation includes unequal distribution of hormonal assessments, smaller sample size for certain markers, potential selection bias, and limited generalizability of thyroid and hormonal dysregulation findings in PCOS. Finally, self-reported symptoms such as acne and anxiety may have introduced reporting bias, potentially underestimating their true prevalence. 7.0 Recommendations Future studies should employ longitudinal designs to better understand the temporal relationship between metabolic factors, hormonal changes, and PCOS progression. Integrating ultrasound-based ovarian assessments alongside AMH measurements could improve the accuracy of PCOS phenotype classification. Additionally, expanding research to include diverse populations will provide a more comprehensive understanding of the condition’s variability across ethnic and geographical backgrounds. Given the high prevalence of metabolic disturbances, routine screening for insulin resistance and cardiovascular risk factors should be incorporated into PCOS management. Finally, public health initiatives should focus on early detection and lifestyle interventions, particularly targeting obesity and insulin resistance, to improve reproductive and metabolic outcomes in women with PCOS. Abbreviations AE-PCOS Androgen Excess and PCOS Society ASRM American Society for Reproductive Medicine BMI Body Mass Index ESHRE European Society of Human Reproduction and Embryology GnRH Gonadotropin-releasing Hormone LHFC Lister Hospital and fertility centre NICHD National Institute of Child Health and Human Development PCOS Polycystic ovary syndrome AMH Anti-Mullerian Hormone. Declarations Author contributions S.Y.L. and M.K.G. conceptualized and designed the study. M.K.G. KA S.A. and E.N.A. acquired the data, carried out the analyses and data interpretation, and drafted the manuscript. S.Y.L. K.A. M.A. E.N.A. C.A.K. G.E.K. P.E. C.O. and J.O.Y. critically reviewed the article. All authors approved the final version of the manuscript. Funding: No funding was received for this work Data availability: The dataset analysed during the study is available from the corresponding author upon a reasonable request. Code availability: Not applicable. Ethical approval and consent to participate Ethicalapproval was sought from the Research Ethics Committee of the University of Health and Allied Sciences (UHAS) with protocol number UHAS-REC A.10 [255] 23-24. Written permission was sought from the management of Lister Hospital and Fertility Center to use the data for this study. Moreover, the Ethics Committee waived consent to participate and publish data since the study design was retrospective that utilized secondary data. This study was carried out following the regulations and guidelines proposed by the Helsinki Declaration. 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Alenzi EO, Alqntash NH, Almajed EH, AlZabin AK. Risk of polycystic ovary syndrome: a population-based analysis of sociodemographic factors, healthcare access, health behaviors, and health status. BMC Womens Health. 2024;24(1):623. Nimbargi V, Mangalath AA, Alias A, Sajith M, Kumdale S. Sociodemographic Characteristics and Clinical Presentation of Infertile Women with Polycystic Ovary Syndrome in a Tertiary Care Hospital. Int J Infertil Fetal Med. 2018;9(1):14–8. WHO. Polycystic ovary syndrome [Internet]. 2025 [cited 2025 Mar 4]. Available from: https://www.who.int/news-room/fact-sheets/detail/polycystic-ovary-syndrome Fraison E, Kostova E, Moran LJ, Bilal S, Ee CC, Venetis C, et al. Metformin versus the combined oral contraceptive pill for hirsutism, acne, and menstrual pattern in polycystic ovary syndrome. Cochrane Database Syst Rev. 2020;2020(8):CD005552. Fedeli V, Catizone A, Querqui A, Unfer V, Bizzarri M. The Role of Inositols in the Hyperandrogenic Phenotypes of PCOS: A Re-Reading of Larner’s Results. Int J Mol Sci. 2023;24(7):6296. Rajbanshi I, Sharma VK, Tuladhar ET, Bhattarai A, Raut M, Dubey RK, et al. Metabolic and biochemical profile in women with polycystic ovarian syndrome attending tertiary care centre of central NEPAL. BMC Womens Health. 2023;23:208. VIDAS. Package_Insert_AMH.pdf. 2016. Donangelo I, Suh SY. Subclinical Hyperthyroidism: When to Consider Treatment. Am Fam Physician. 2017;95(11):710–6. Saadia Z. Follicle Stimulating Hormone (LH: FSH) Ratio in Polycystic Ovary Syndrome (PCOS) - Obese vs. Non- Obese Women. Med Arch. 2020;74(4):289–93. Ali N, Kathak RR, Fariha KA, Taher A, Islam F. Prevalence of dyslipidemia and its associated factors among university academic staff and students in Bangladesh. BMC Cardiovasc Disord. 2023;23(1):366. Coulman K, Toran SS. The Conversation. 2020 [cited 2025 Mar 4]. Body mass index may not be the best indicator of our health – how can we improve it? Available from: http://theconversation.com/body-mass-index-may-not-be-the-best-indicator-of-our-health-how-can-we-improve-it-143155 Ajmal N, Khan SZ, Shaikh R. Polycystic ovary syndrome (PCOS) and genetic predisposition: A review article. Eur J Obstet Gynecol Reprod Biol X. 2019;3:100060. Bozdag G, Mumusoglu S, Zengin D, Karabulut E, Yildiz BO. The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. Hum Reprod Oxf Engl. 2016;31(12):2841–55. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, et al. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Fertil Steril. 2018;110(3):364–79. Keskin M, Arsoy HA, Kara O, Sarandol E, Koca N, Yilmaz Y. Impact of Comorbid Polycystic Ovary Syndrome on Clinical and Laboratory Parameters in Female Adolescents with Metabolic Dysfunction-Associated Steatotic Liver Disease: A Cross-Sectional Study. J Clin Med. 2024;13(19):5885. Rasquin LI, Anastasopoulou C, Mayrin JV. Polycystic Ovarian Disease. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Mar 1]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK459251/ Ding T, Hardiman PJ, Petersen I, Wang FF, Qu F, Baio G. The prevalence of polycystic ovary syndrome in reproductive-aged women of different ethnicity: a systematic review and meta-analysis. Oncotarget. 2017;8(56):96351–8. Rahmatnezhad L, Moghaddam-Banaem L, Behroozi-Lak T, Shiva A, Rasouli J. Association of insulin resistance with polycystic ovary syndrome phenotypes and patients’ characteristics: a cross-sectional study in Iran. Reprod Biol Endocrinol RBE. 2023;21:113. Moran LJ, Misso ML, Wild RA, Norman RJ. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Hum Reprod Update. 2010;16(4):347–63. Ranathunga I, Athukorala TG, Sumanatilleke MR, Somasundaram NP. Evaluation of socio-demographic and clinical characteristics of PCOS patients attending a tertiary care institute in Colombo. BMC Endocr Disord. 2022;22(1):289. Sanchez-Garrido MA, Tena-Sempere M. Metabolic dysfunction in polycystic ovary syndrome: Pathogenic role of androgen excess and potential therapeutic strategies. Mol Metab. 2020;35:100937. Dominic N, Sharma L, Mohindra N, Dabadghao P. Prevalence of polycystic ovary syndrome and its clinical and hormonal profile in young females with type 1 diabetes mellitus: experience from a teaching institution of India. Endocrine. 2023;82(2):303–10. Codner E, Merino PM, Tena-Sempere M. Female reproduction and type 1 diabetes: from mechanisms to clinical findings. Hum Reprod Update. 2012;18(5):568–85. Mangalath AA, Nimbargi, V, Alias A, Sajith M, Kumdale S. Sociodemographic Characteristics and Clinical Presentation of Infertile Women with Polycystic Ovary Syndrome in a Tertiary Care Hospital. Int J Infertil Fetal Med. 2018;9(1):14–8. Lim SS, Kakoly NS, Tan JWJ, Fitzgerald G, Bahri Khomami M, Joham AE, et al. Metabolic syndrome in polycystic ovary syndrome: a systematic review, meta-analysis and meta-regression. Obes Rev Off J Int Assoc Study Obes. 2019;20(2):339–52. Azziz R, Carmina E, Chen Z, Dunaif A, Laven JSE, Legro RS, et al. Polycystic ovary syndrome. Nat Rev Dis Primer. 2016;2:16057. Dumesic DA, Oberfield SE, Stener-Victorin E, Marshall JC, Laven JS, Legro RS. Scientific Statement on the Diagnostic Criteria, Epidemiology, Pathophysiology, and Molecular Genetics of Polycystic Ovary Syndrome. Endocr Rev. 2015;36(5):487–525. Dewailly D, Lujan ME, Carmina E, Cedars MI, Laven J, Norman RJ, et al. Definition and significance of polycystic ovarian morphology: a task force report from the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2014;20(3):334–52. Singh S, Pal N, Shubham S, Sarma DK, Verma V, Marotta F, et al. Polycystic Ovary Syndrome: Etiology, Current Management, and Future Therapeutics. J Clin Med. 2023;12(4):1454. Glintborg D, Rubin KH, Nybo M, Abrahamsen B, Andersen M. Increased risk of thyroid disease in Danish women with polycystic ovary syndrome: a cohort study. Endocr Connect. 2019;8(10):1405–15. Palomba S, Colombo C, Busnelli A, Caserta D, Vitale G. Polycystic ovary syndrome and thyroid disorder: a comprehensive narrative review of the literature. Front Endocrinol. 2023;14:1251866. Rojhani E, Rahmati M, Firouzi F, Saei Ghare Naz M, Azizi F, Ramezani Tehrani F. Polycystic Ovary Syndrome, Subclinical Hypothyroidism, the Cut-Off Value of Thyroid Stimulating Hormone; Is There a Link? Findings of a Population-Based Study. Diagnostics. 2023;13(2):316. Singla R, Gupta Y, Khemani M, Aggarwal S. Thyroid disorders and polycystic ovary syndrome: An emerging relationship. Indian J Endocrinol Metab. 2015;19(1):25–9. Rosenfield RL, Ehrmann DA. The Pathogenesis of Polycystic Ovary Syndrome (PCOS): The Hypothesis of PCOS as Functional Ovarian Hyperandrogenism Revisited. Endocr Rev. 2016;37(5):467–520. Davoudi Z, Araghi F, Vahedi M, Mokhtari N, Gheisari M. Prolactin Level in Polycystic Ovary Syndrome (PCOS): An approach to the diagnosis and management. Acta Bio Medica Atenei Parm. 2021;92(5):e2021291. Mastnak L, Herman R, Ferjan S, Janež A, Jensterle M. Prolactin in Polycystic Ovary Syndrome: Metabolic Effects and Therapeutic Prospects. Life. 2023;13(11):2124. Piouka A, Farmakiotis D, Katsikis I, Macut D, Gerou S, Panidis D. Anti-Müllerian hormone levels reflect severity of PCOS but are negatively influenced by obesity: relationship with increased luteinizing hormone levels. Am J Physiol-Endocrinol Metab. 2009;296(2):E238–43. Ahmad AK, Kao CN, Quinn M, Lenhart N, Rosen M, Cedars MI, et al. Differential rate in decline in ovarian reserve markers in women with polycystic ovary syndrome compared with control subjects: results of a longitudinal study. Fertil Steril. 2018;109(3):526–31. Delamuta LC, Fassolas G, Dias JA, Henrique LF de O, Izzo FPM, Izzo CR. Antimüllerian hormone levels and IVF outcomes in polycystic ovary syndrome women: a scoping review. JBRA Assist Reprod. 2024;28(2):299–305. Moolhuijsen LME, Visser JA. Anti-Müllerian Hormone and Ovarian Reserve: Update on Assessing Ovarian Function. J Clin Endocrinol Metab. 2020;105(11):3361–73. Kloos J, Perez J, Weinerman R. Increased body mass index is negatively associated with ovarian reserve as measured by anti-Müllerian hormone in patients with polycystic ovarian syndrome. Clin Obes. 2024;14(3):e12638. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviews received at journal 29 May, 2025 Reviews received at journal 23 May, 2025 Reviewers agreed at journal 17 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 06 May, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6388647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454724730,"identity":"39040366-cc5a-409d-b328-65b2e5c7a700","order_by":0,"name":"Sylvester Yao 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21:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6388647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6388647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82713331,"identity":"5d608a10-0fc7-4e3e-b100-a7f63c020b33","added_by":"auto","created_at":"2025-05-14 11:51:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between age and AMH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP value is significant at p\u0026lt;0.05, n; number of participants, r; correlation coefficient, R; regression coefficient\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6388647/v1/d2efddbcb1a6731137e5c5d4.png"},{"id":82712748,"identity":"ca830863-0276-416d-ae09-c65ea2e54db8","added_by":"auto","created_at":"2025-05-14 11:43:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between body mass index and AMH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP value is significant at P\u0026lt;0.05, n; number of participants, r; corelation coefficient, R; regression coefficient\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6388647/v1/46651d178616728f23d246f4.png"},{"id":82713330,"identity":"61c9ea69-12db-4eeb-8891-f70420c6514f","added_by":"auto","created_at":"2025-05-14 11:51:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4.3: PCOS phenotypes among study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6388647/v1/6e6127bb2f9c64244340090b.png"},{"id":82714139,"identity":"87a6112e-0a64-4375-98cb-ebd69841f4cb","added_by":"auto","created_at":"2025-05-14 11:59:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1469903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6388647/v1/60a6ff28-5489-41ef-809b-632cc5629919.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic, clinical characteristics, polycystic ovarian syndrome phenotypes and predictors of anti-mullerian hormone among women with PCOS at a Fertility Center in Ghana: A 5-year retrospective study","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a heterogeneous illness characterized by a mix of signs and symptoms of androgen excess and ovarian dysfunction and it is one of the leading causes of infertility worldwide (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The diagnosis is made after ruling out other specific conditions including hyperprolactinemia and non-classic congenital adrenal hyperplasia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, the definition of PCOS has been disputed in a variety of disciplines due to the variation in its presentation. For researchers working in both clinical and fundamental science settings, understanding the aetiology of PCOS and differentiating between primary pathological alterations and secondary environmental disturbances remains a persistent difficulty (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePCOS is the most common endocrine disorder affecting women of reproductive age worldwide, with the prevalence estimated to be in the range of 4% and 12% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In Nigeria, the prevalence of PCOS among infertile women ranges from 16.7\u0026ndash;27.6%, depending on the diagnostic criteria used (National Institute of Health, Rotterdam Classification, or Androgen Excess Society) (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, a study in Sudan among infertile women found that 32% met the Rotterdam criteria for PCOS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Stein and Leventhal (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), PCOS which was regarded as a gynecological disorder, was now considered a complex endocrinopathy in women of reproductive age owing to its association with multiple metabolic co-morbidities. Women with PCOS typically exhibit hyperandrogenism along with oligomenorrhea, amenorrhea, or infertility (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For women with PCOS, three sets of criteria namely; National Institute of Health, Rotterdam Classification, and Androgen Excess Society have been established. For each of these criteria, different combinations of ovulatory failure, polycystic ovarian morphologic characteristics, and hyperandrogenism constitute unique phenotypes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, PCOS can be classified into four distinct phenotypes based on the presence or absence of the three primary features: hyperandrogenism (HA), ovulatory dysfunction (OD), and polycystic ovarian morphology (PCOM) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The classic phenotype which combines the three main features (HA\u0026thinsp;+\u0026thinsp;OD\u0026thinsp;+\u0026thinsp;PCOM) has been associated with the most severe metabolic and reproductive disturbances (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The second phenotype combines hormonal imbalances and menstrual irregularities but lacks polycystic ovarian morphology (HA\u0026thinsp;+\u0026thinsp;OD) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The third phenotype features androgen excess and ovarian abnormalities without ovulatory dysfunction (HA\u0026thinsp;+\u0026thinsp;PCOM) while the fourth phenotype (OD\u0026thinsp;+\u0026thinsp;PCOM) is considered the mildest form, presenting with ovulatory dysfunction and polycystic ovarian morphology but without signs of hyperandrogenism (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe management of clinically relevant phenotypes requires a tailored approach that addresses both reproductive and metabolic concerns. Lifestyle modifications, including diet and exercise, are the first-line approach, especially for overweight individuals, as weight loss improves ovulation and insulin sensitivity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Pharmacological treatments include hormonal contraceptives for cycle regulation and hyperandrogenism, ovulation induction agents for fertility, and insulin sensitizers like metformin for metabolic issues (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In severe cases, anti-androgens such as spironolactone may be used (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Emerging therapies like inositol supplementation and glucagon-like peptide (GLP)-1 receptor agonists show promise in addressing metabolic dysfunction (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNonetheless, there is currently a gap in research focusing on the demographic and clinical profiles of women with PCOS within the Ghanaian population. Moreover, information about the clinically relevant PCOS phenotypes is lacking and this hinders the development of personalized treatment strategies potentially contributing to suboptimal outcomes in affected women. It is against this background that we sought to retrospectively analyze the demographic, clinical characteristics, PCOS phenotypes and the factors associated with anti-mullerian hormone (AMH) levels in women with clinically diagnosed PCOS at Lister Hospital and Fertility Center in Accra, Ghana based on dated extracted from 2019 to 2023.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThe study was a hospital-based retrospective analysis of secondary data collected on women presenting with PCOS at the Lister Hospital and Fertility Center (LHFC) in Accra. Archival data from January 2019 to December 2023 was extracted for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study site\u003c/h2\u003e \u003cp\u003eThe study was carried out at the LHFC, a subsidiary of Lister Medical Services, which was established on July 1, 2004. It is an ultra-modern private medical center among the most technologically advanced hospitals in West Africa. LHFC has 25 registered beds and provides the following services; laboratory, imaging, nursing- emergency, in-patient, out-patient, public health, theatre-, pharmacy, and fertility. The laboratory department provides a wide range of investigations including hormonal assays, full blood count tests, thyroid function tests, liver function tests, and lipid profiles as well as microbiological tests. The hospital currently has an average attendance of over 2000 annually.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study population and sampling technique\u003c/h2\u003e \u003cp\u003eThe study population included women diagnosed of PCOS who sought medical attention at the hospital between January 2019 and December 2023. A convenient sampling technique was used for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Inclusion and exclusion criteria\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Inclusion criteria\u003c/h2\u003e \u003cp\u003ePremenopausal women aged 18 years and older with PCOS who had records available for review at the hospital during the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Exclusion criteria\u003c/h2\u003e \u003cp\u003eWomen without PCOS diagnosis, aged below 18 years, with a history of endocrinal disorders including those that may mimic PCOS such as Cushion syndrome, hyperprolactinaemia, thyroid disease, congenital adrenal hyperplasia, non-classic adrenal hyperplasia, adrenal secreting tumour, idiopathic hirsutism, and idiopathic hyperandrogenism.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sample size determination\u003c/h2\u003e \u003cp\u003eThe sample size was calculated using Cochran\u0026rsquo;s formula with prevalence of 16.7% (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003en= (Z\u003csup\u003e2\u003c/sup\u003e x p x (1-p)/e\u003csup\u003e2\u003c/sup\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e= (1.962 x 0.167 x 0.93)/0.052\u0026thinsp;=\u0026thinsp;100\u003c/p\u003e \u003cp\u003eWhere Z\u0026thinsp;=\u0026thinsp;1.96 (at 95% confidence interval), e\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;prevalence of 16.7%\u003c/p\u003e \u003cp\u003eThe minimum sample size was 214. However, to improve the statistical power, a total of 242 records were included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data collection instrument and procedure\u003c/h2\u003e \u003cp\u003eData were collected using a data extraction sheet. Socio-demographic data (age, occupation, marital status, religion and level of education, employment status) and clinical information including laboratory variables (hormonal profile), body mass index, menstrual irregularities, hirsutism, alopecia, acne, etc, as well as the presence of complications such as diabetes mellitus, hypertension, and cardiovascular disease, were retrieved from the archival records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Determination of hormone levels\u003c/h2\u003e \u003cp\u003eAll hormonal assays were performed on mini VIDAS\u0026reg; automated equipment (bioM\u0026eacute;rieux, France). The assay is based on the principle of immunoassay sandwich method with final fluorescent detection. The sample to be measured is transferred into wells containing a specific antibody labelled with alkaline phosphatase (conjugate). The sample/conjugate mixture is repeatedly cycled through the solid phase receptacle, allowing the target hormone to bind to the antibodies coated on the solid phase receptacle interior while also forming a complex with the conjugate. Unbound components are removed during the washing steps. In the final detection step, the substrate (4-methylumbelliferyl phosphate) is introduced and cycled through the solid phase receptacle. The conjugate enzyme catalyzes the hydrolysis of the substrate into a fluorescent product (4-methylumbelliferone), which is measured at 450 nm. The fluorescence intensity is directly proportional to the concentration of the hormone in the sample. Upon completion of the assay, the instrument automatically calculates the results based on a stored calibration curve and generates a printed report (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Definitions of operational terms\u003c/h2\u003e \u003cp\u003eElevated AMH level was defined as serum levels greater than 3ng/ml. Subclinical hyperthyroidism was defined as low TSH (\u0026lt;\u0026thinsp;0.39mIU/L) and normal T\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e4\u003c/sub\u003e levels while subclinical hypothyroidism was defined as high TSH (\u0026gt;\u0026thinsp;6.16mIU/L) and normal T\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e4\u003c/sub\u003e levels (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Low FSH level was defined as FSH less than 2.0mIU/ml. Elevated LH level was defined as LH greater than 26.0mIU/ml. Elevated LH/FSH ratio was defined as LH/FSH ratio greater than 2. Low LH/FSH ratio was defined as LH/FSH ratio lower than 1.1 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Hyperprolactinaemia was defined as serum prolactin levels greater than 25ng/ml. Dyslipidaemia was defined using the NCEP-ATP III criteria (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). BMI (kg/m\u003csup\u003e3\u003c/sup\u003e) was classified into normal weight; 18.5\u0026ndash;24.9, overweight; 25.0-29.9, obesity class I; 30.0-34.9, obesity class II; 35.0-39.9 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Data handling and analysis\u003c/h2\u003e \u003cp\u003e Data were obtained into an Excel Spreadsheet, cleaned, checked for completeness, and exported into Statistical Package for the Social Sciences (SPSS) version 26.0 and MedCalc version 22.0 for analysis. Descriptive statistics were used to summarize the socio-demographic, biochemical, and clinical characteristics of study participants. Continuous variables were described as median with minimum and maximum values. Categorical variables were described as the frequency and corresponding proportions. Correlation and linear regression model analyses were performed to determine the predictors of AMH levels. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below, a total of 242 records of PCOS patients were reviewed for the 5 years under review. The median age of participants was 31 years, with the least and maximum ages being 18 and 45 years, respectively while most participants (31.4%, n\u0026thinsp;=\u0026thinsp;76) were within the 31\u0026ndash;35 years category. The majority of participants were married (73.1%, n\u0026thinsp;=\u0026thinsp;177), 177 had attained tertiary level education (73.1%, n\u0026thinsp;=\u0026thinsp;177) and more than half were Christians (61.2%, n\u0026thinsp;=\u0026thinsp;148). The employment status showed that a majority of participants (71.1%, n\u0026thinsp;=\u0026thinsp;172) were unemployed whereas only 28.9% (n\u0026thinsp;=\u0026thinsp;70) were employed.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic characteristics of study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (minimum-maximum)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.0 (18.0\u0026ndash;45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligious affiliation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChristianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIslam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHinduism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData presented as the frequency and the corresponding proportions or median with minimum and maximum values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e below, the clinical characteristics indicate that diabetes mellitus was present in 14.9% of participants, with 12.0% (n\u0026thinsp;=\u0026thinsp;29) diagnosed with type 2 diabetes mellitus while 2.9% (n\u0026thinsp;=\u0026thinsp;7) had type 1 diabetes mellitus. Approximately, 82.6% of participants were either overweight or obese. Menstrual irregularities were prevalent, with 48.3% (n\u0026thinsp;=\u0026thinsp;117) experiencing amenorrhea and 44.2% (n\u0026thinsp;=\u0026thinsp;107) reporting oligomenorrhea, while dysmenorrhea was present in 7.4% (n\u0026thinsp;=\u0026thinsp;18). Hirsutism was observed in 14.5% (n\u0026thinsp;=\u0026thinsp;35) of participants, with no clinical presentations of alopecia, acne and anxiety.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical characteristics of study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity class I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity class II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenstrual History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDysmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHirsutism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlopecia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcne\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData presented as the frequency and the corresponding proportions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHormonal characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below, all participants had elevated anti-M\u0026uuml;llerian hormone (AMH) (100%, n\u0026thinsp;=\u0026thinsp;242). Subclinical hypothyroidism and subclinical hyperthyroidism were present in 0.4%, n\u0026thinsp;=\u0026thinsp;1 and 0.8%, n\u0026thinsp;=\u0026thinsp;2, respectively. Elevated luteinizing hormone-follicle stimulating hormone (LH/FSH) ratio and prolactin levels were observed in 29.4%, n\u0026thinsp;=\u0026thinsp;10 and 20.8%, n\u0026thinsp;=\u0026thinsp;41 of study participants.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHormonal characteristics of study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHormones\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnti-Mullerian Hormone (ng/ml) (n\u0026thinsp;=\u0026thinsp;242)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid Stimulating Hormone (mIU/ml) (n\u0026thinsp;=\u0026thinsp;240)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubclinical hypothyroidism (High TSH, Normal T\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubclinical hyperthyroidism (Low TSH, Normal T\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuteinizing Hormone (mIU/ml) (n\u0026thinsp;=\u0026thinsp;44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollicle Stimulating Hormone (mIU/ml) (n\u0026thinsp;=\u0026thinsp;35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuteinizing Hormone -Follicle Stimulating Hormone Ratio (n\u0026thinsp;=\u0026thinsp;34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactin (ng/ml) (n\u0026thinsp;=\u0026thinsp;197)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData presented as the frequency and the corresponding proportions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Age and Anti-Mullerian Hormone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;1 below, there was no significant association between age and AMH levels (r\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;0.714) from the correlation and linear regression analyses.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Body Mass Index and Anti- Mullerian Hormone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom Fig.\u0026nbsp;2, the scatter plot shows a significant positive correlation between BMI and AMH levels (r\u0026thinsp;=\u0026thinsp;0.65; p\u0026thinsp;\u0026lt;\u0026thinsp;0.65). Moreover, for every 1 kgm\u003csup\u003e2\u003c/sup\u003e change in BMI results in 0.73ng/ml increase in AMH (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCOS phenotypes among study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;3, the distribution of PCOS phenotypes shows that the majority (85.5%, n\u0026thinsp;=\u0026thinsp;207) presented with the \u0026quot;elevated AMH\u0026thinsp;+\u0026thinsp;oligo-anovulation\u0026quot; phenotype. 7.4%, n\u0026thinsp;=\u0026thinsp;18 exhibited the \u0026quot;elevated AMH\u0026thinsp;+\u0026thinsp;Hirsutism\u0026quot; phenotype, while 7.0% (n\u0026thinsp;=\u0026thinsp;17) had the classic phenotype (Oligo-anovulation\u0026thinsp;+\u0026thinsp;Hirsutism\u0026thinsp;+\u0026thinsp;elevated AMH).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) predominantly affects women of reproductive age, with varying age distributions across different populations (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In our study, the median age of 31 years suggests that PCOS is most commonly diagnosed in women in their late twenties to early thirties. This observation aligns with a study by Bozdag and colleagues (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) who indicated that PCOS prevalence peaks between 25 and 34 years, coinciding with the reproductive years when symptoms such as menstrual irregularities, infertility, and metabolic disturbances become more pronounced.\u003c/p\u003e \u003cp\u003eMoreover, the finding that the majority of participants (31.4%) fell within the 31\u0026ndash;35 years range agrees with a previous study highlighting that women often seek medical attention for PCOS-related issues, such as difficulty in conception, during this period (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our findings however contradict those of Alenzi and co (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) who found a majority of women (31.5%) with PCOS between 18 and 22 years. The disparity between our findings and those reported by Alenzi and co (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), could be attributed to several factors, including differences in study populations, healthcare access, and diagnostic patterns. According to Keskin and colleagues (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), younger women between the ages of 18\u0026ndash;22 years often seek medical attention when experiencing severe symptoms such as irregular menstruation, acne, or excessive hair growth, leading to higher diagnosis rates in this age group.\u003c/p\u003e \u003cp\u003eEarly recognition of PCOS in younger women, particularly in adolescents, is crucial for timely intervention to prevent long-term complications such as type 2 diabetes mellitus and cardiovascular disease (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Meanwhile, the presence of PCOS in older women nearing menopause suggests that metabolic and endocrine consequences may persist beyond the typical reproductive years, necessitating lifelong management strategies. This assertion was corroborated by Ding \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) who indicated that women with PCOS often continue to exhibit hyperandrogenism and insulin resistance even after menopause, further emphasizing the importance of ongoing medical care.\u003c/p\u003e \u003cp\u003eThe high prevalence of metabolic disturbances among participants with PCOS agrees with existing evidence linking the condition to insulin resistance and obesity (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In our study, 14.9% of participants had diabetes mellitus, with type 2 diabetes mellitus (T2DM) being the predominant form (12.0%). The finding is consistent with a systematic review conducted by Moran \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) who reported the prevalence of T2DM in women with PCOS ranging from 7\u0026ndash;40%, depending on ethnicity, age, and BMI. However, the findings contradict a study by Ranathunga and colleagues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) who reported a lower rate of diabetes mellitus (6.7%) in women with PCOS. Insulin resistance, a key pathological feature of PCOS, contributes to hyperinsulinemia, which exacerbates ovarian androgen production and metabolic dysfunction, thereby increasing the risk of T2DM (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The relatively lower prevalence of T1DM among women with PCOS in our study is somewhat expected as T1DM is an autoimmune disorder that is not directly linked to the metabolic and endocrine dysregulation observed in PCOS (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Nevertheless, a previous study suggests that PCOS may still occur in women with T1DM due to insulin therapy-induced hyperinsulinemia, which affects ovarian function (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high prevalence of overweight and obesity (82.6%) among participants in our study reinforces the well-established link between PCOS and weight gain, particularly in populations with high insulin resistance. This finding is slightly higher than the 76.7% reported by Ranathunga \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) in a study conducted in Colombo and 53.3% reported by Mangalath \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) in India, suggesting possible differences in lifestyle, genetic predisposition, or healthcare access between the study populations. Obesity is known to exacerbate PCOS symptoms by worsening insulin resistance, increasing androgen levels, and contributing to metabolic dysfunction, which in turn heightens the risk of diabetes and cardiovascular diseases (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The higher prevalence of obesity in our study may also indicate environmental and dietary factors that promote weight gain, including sedentary lifestyles and high-calorie diets, which are increasingly prevalent in Sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eInterestingly, no cases of alopecia, acne, or anxiety were reported among our study participants, which contrasts with prior studies that frequently identify these as common clinical features of PCOS (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This discrepancy may be attributed to variations in genetic predisposition, environmental factors, or differences in participant self-reporting and clinical assessment methods. Additionally, healthcare-seeking behaviour may play a role, as women with metabolic symptoms might prioritize seeking medical attention for obesity and diabetes mellitus rather than dermatological or psychological concerns.\u003c/p\u003e \u003cp\u003eThe distribution of PCOS phenotypes in this study reveals that the majority (85.5%) presented with the elevated AMH\u0026thinsp;+\u0026thinsp;oligo-anovulation phenotype, 7.4% exhibited elevated AMH\u0026thinsp;+\u0026thinsp;hirsutism phenotype, while 7.0% had the classic phenotype (oligo-anovulation\u0026thinsp;+\u0026thinsp;hirsutism\u0026thinsp;+\u0026thinsp;elevated AMH). Our study findings contrast with Tatar \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) who reported 47.9% of the classic phenotype, 37.5% of ovarian dysfunction\u0026thinsp;+\u0026thinsp;hyperandrogenism phenotype, and 4.1% of ovarian dysfunction\u0026thinsp;+\u0026thinsp;polycystic ovarian morphology phenotype. This discrepancy may largely stem from differences in the diagnostic criteria and methods used to define PCOS phenotypes. While our study relied on elevated AMH levels as a surrogate marker for ovarian dysfunction, Tatar e\u003cem\u003et al\u003c/em\u003e (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) incorporated ultrasound-detected polycystic ovarian morphology as part of their diagnostic framework. Such methodological variations can lead to differing classifications, as AMH levels and ovarian morphology, although correlated, may not accurately reflect the underlying ovarian pathology.\u003c/p\u003e \u003cp\u003eFurthermore, the variations in PCOS phenotypes may be influenced by lifestyle, environmental, and metabolic differences across populations. Hyperandrogenic phenotypes are more common in Western populations, while ovulatory dysfunction with fewer androgenic symptoms is more prevalent in Asia and the Middle East (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The high prevalence of elevated AMH\u0026thinsp;+\u0026thinsp;oligo-anovulation phenotype in our study may be due to the role of AMH as a key marker of ovarian dysfunction in PCOS (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). These findings emphasize the heterogeneity of PCOS and the need for tailored management strategies. For instance, women with predominant ovarian dysfunction may benefit more from ovulation-inducing therapies, whereas those with hyperandrogenic features might require additional interventions targeting androgen excess (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of elevated AMH levels (100%) among participants in our study underscores the strong association between AMH and polycystic ovary syndrome (PCOS). AMH is a hormone secreted by small antral follicles, and it is frequently elevated in PCOS due to an increased follicular pool and disrupted folliculogenesis (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Our finding is somewhat expected as AMH serves as a valuable biomarker for diagnosing PCOS, particularly in distinguishing it from other causes of menstrual irregularities (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the prevalence of thyroid dysfunction was low in our study, with 0.4% and 0.8% of participants exhibiting subclinical hypothyroidism and subclinical hyperthyroidism, respectively. Contrary to our study, Mangalath \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) found 16% each of hypothyroidism and hyperthyroidism. In 2019, a study from Denmark reported that the risk of thyroid disease in PCOS patients was 2.5 times higher than in patients without PCOS (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In addition, a review by Palomba \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), posits that PCOS and thyroid disorders are closely related, and their coexistence may identify patients with a higher reproductive and metabolic risk. Interestingly, however, Rojhani \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) found no association between PCOS status and subclinical hypothyroidism. These findings are in line with a study suggesting that thyroid dysfunction is not a major feature of PCOS, although some studies have noted a slightly higher prevalence, potentially due to shared endocrine pathways and autoimmune associations (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, an elevated luteinizing hormone (LH)/follicle-stimulating hormone (FSH) ratio was observed in 29.4% of participants, while 20.8% had hyperprolactinaemia. The findings in relation to LH/FSH ratio contrasts with an earlier report of a higher prevalence of LH/FSH dysregulation, exceeding 40%, particularly in the classic PCOS phenotypes (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). The relatively lower proportion in our study may reflect phenotypic differences, as hyperandrogenic features are often more pronounced in Western populations, where LH hypersecretion is more frequently reported (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Hyperprolactinemia, though not a defining characteristic of PCOS, has been documented in a subset of patients, likely due to chronic oestrogen stimulation or stress-related hypothalamic dysfunction (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study found no significant association between age and AMH levels (r\u0026thinsp;=\u0026thinsp;0.02, P\u0026thinsp;=\u0026thinsp;0.714). This is inconsistent with Piouka \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) who found age to be negatively correlated with AMH (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.215, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) indicating that ovarian reserve diminishes over time (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). However, the absence of a significant correlation in our study could be attributed to the relatively narrow age range of participants or the influence of PCOS pathophysiology, where AMH remains persistently elevated despite advancing age.\u003c/p\u003e \u003cp\u003eConversely, our study observed a significant positive correlation between BMI and AMH levels (r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a 0.73 ng/ml increase in AMH for every 1 kg/m\u0026sup2; increase in BMI possibly due to an increased follicular pool and impaired follicular maturation as obesity is associated with increased AMH levels in women with PCOS. Our findings contrasts with a study by Piouka \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) who found BMI to be negatively correlated with AMH, with a -0.71ng/ml decrease in AMH for every 1 kg/m\u0026sup2; increase in BMI. Another study also revealed that AMH significantly decreased as BMI increased (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). The disparity regarding the relationship between BMI and AMH levels in PCOS patients may be attributed to factors such as differences in study populations, methodologies, and underlying metabolic characteristics. Additionally, Piouka \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and Kloos \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) may have included participants with more advanced metabolic dysfunction, where prolonged obesity-induced ovarian damage leads to reduced follicular reserve and lower AMH secretion.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eOur study provides insight into the demographic, clinical, and hormonal characteristics of women with PCOS, highlighting weight management as a possible mitigating strategy for hyperactive ovarian disease. The study further emphasizes the need for personalized management strategies to address the diverse manifestations of PCOS and to mitigate long-term complications such as infertility, diabetes, and cardiovascular disease.\u003c/p\u003e"},{"header":"6.0 Limitations","content":"\u003cp\u003eDespite the strength of our study regarding a review conducted over five years, certain limitations have been acknowledged. The cross-sectional nature of our study prevents causal inferences. Additionally, the reliance on AMH as a primary diagnostic marker without incorporating an ultrasound assessment of ovarian morphology may have influenced the phenotype classifications. The study population may not be entirely representative of broader PCOS populations, as factors such as ethnicity, lifestyle, and healthcare access can influence symptom presentation. Another limitation includes unequal distribution of hormonal assessments, smaller sample size for certain markers, potential selection bias, and limited generalizability of thyroid and hormonal dysregulation findings in PCOS. Finally, self-reported symptoms such as acne and anxiety may have introduced reporting bias, potentially underestimating their true prevalence.\u003c/p\u003e"},{"header":"7.0 Recommendations","content":"\u003cp\u003eFuture studies should employ longitudinal designs to better understand the temporal relationship between metabolic factors, hormonal changes, and PCOS progression. Integrating ultrasound-based ovarian assessments alongside AMH measurements could improve the accuracy of PCOS phenotype classification. Additionally, expanding research to include diverse populations will provide a more comprehensive understanding of the condition\u0026rsquo;s variability across ethnic and geographical backgrounds. Given the high prevalence of metabolic disturbances, routine screening for insulin resistance and cardiovascular risk factors should be incorporated into PCOS management. Finally, public health initiatives should focus on early detection and lifestyle interventions, particularly targeting obesity and insulin resistance, to improve reproductive and metabolic outcomes in women with PCOS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAE-PCOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAndrogen Excess and PCOS Society\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eASRM\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAmerican Society for Reproductive Medicine\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBody Mass Index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eESHRE\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEuropean Society of Human Reproduction and Embryology\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGnRH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGonadotropin-releasing Hormone\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLHFC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLister Hospital and fertility centre\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNICHD\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNational Institute of Child Health and Human Development\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePolycystic ovary syndrome\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAMH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAnti-Mullerian Hormone.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eS.Y.L. and M.K.G. conceptualized and designed the study. M.K.G. KA S.A. and E.N.A. acquired the data, carried out the analyses and data interpretation, and drafted the manuscript. S.Y.L. K.A. M.A. E.N.A. C.A.K. G.E.K. P.E. C.O. and J.O.Y. critically reviewed the article. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received for this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe dataset analysed during the study is available from the corresponding author upon a reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eapproval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthicalapproval was sought from the Research Ethics Committee of the University of Health and Allied Sciences (UHAS) with protocol number UHAS-REC A.10 [255] 23-24. Written permission was sought from the management of Lister Hospital and Fertility Center to use the data for this study. Moreover, the Ethics Committee waived consent to participate and publish data since the study design was retrospective that utilized secondary data. This study was carried out following the regulations and guidelines proposed by the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent to participate and publish data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEscobar-Morreale HF. Polycystic ovary syndrome: Definition, aetiology, diagnosis and treatment. 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Am J Physiol-Endocrinol Metab. 2009;296(2):E238\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad AK, Kao CN, Quinn M, Lenhart N, Rosen M, Cedars MI, et al. Differential rate in decline in ovarian reserve markers in women with polycystic ovary syndrome compared with control subjects: results of a longitudinal study. Fertil Steril. 2018;109(3):526\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelamuta LC, Fassolas G, Dias JA, Henrique LF de O, Izzo FPM, Izzo CR. Antim\u0026uuml;llerian hormone levels and IVF outcomes in polycystic ovary syndrome women: a scoping review. JBRA Assist Reprod. 2024;28(2):299\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoolhuijsen LME, Visser JA. Anti-M\u0026uuml;llerian Hormone and Ovarian Reserve: Update on Assessing Ovarian Function. J Clin Endocrinol Metab. 2020;105(11):3361\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKloos J, Perez J, Weinerman R. Increased body mass index is negatively associated with ovarian reserve as measured by anti-M\u0026uuml;llerian hormone in patients with polycystic ovarian syndrome. Clin Obes. 2024;14(3):e12638.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6388647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6388647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePolycystic ovarian syndrome (PCOS) is most prevalent among women of reproductive age and is characterized by heterogeneous clinical presentations and demographic variability. This five-year review examined the demographic, clinical characteristics, PCOS phenotypes, and the factors associated with anti-mullerian hormone (AMH) in women with PCOS at a Fertility Center in Accra.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eThe study employed a hospital-based retrospective analysis involving women with PCOS at Lister Hospital and Fertility Center, with data extracted from January 2019 to December 2023. Descriptive statistics were used to summarize socio-demographic, biochemical, and clinical parameters. The Rotterdam Criteria were used to classify clinically relevant PCOS phenotypes. Correlation and linear regression analyses were performed to determine predictors of AMH levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 242 PCOS patients’ records were reviewed. The median age was 31 years. Approximately, 14.9% had diabetes mellitus, 82.6% were overweight/obese, and 48.3% experienced amenorrhea. All participants (100%) had elevated AMH, with 29.4% exhibiting elevated luteinizing hormone to follicle stimulating hormone (LH/FSH) ratio, and 20.8% had hyperprolactinaemia. 85.5%, (n=207) had \"elevated AMH + oligo-anovulation\" phenotype. 7.4% (n=18) exhibited \"elevated AMH + hirsutism\" phenotype, while 7.0% (n=17) had the classic phenotype (oligo-anovulation + hirsutism + elevated AMH). BMI was correlated with AMH (r=0.65, p\u0026lt;0.001), with each kg/m² change in BMI resulting in a 0.73 ng/ml increase in AMH levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study provided insight into the demographic and clinical characteristics of women with PCOS, highlighting weight management as a mitigating strategy for hyperactive ovarian disease and need for personalized management strategies to prevent long-term complications.\u003c/p\u003e","manuscriptTitle":"Demographic, clinical characteristics, polycystic ovarian syndrome phenotypes and predictors of anti-mullerian hormone among women with PCOS at a Fertility Center in Ghana: A 5-year retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 11:35:33","doi":"10.21203/rs.3.rs-6388647/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-26T18:47:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T18:00:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T18:10:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T14:24:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26446842237827730266927520496894844153","date":"2025-05-17T07:51:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78334480816641839897003078221485660807","date":"2025-05-15T19:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246473079312395090434627037817607688870","date":"2025-05-10T15:45:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T15:38:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T09:25:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T00:35:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Medicine","date":"2025-04-30T00:34:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2eff7e0-ff7d-4bb0-b52f-bc6055f07027","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T11:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 11:35:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6388647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6388647","identity":"rs-6388647","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}